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第41届中国控制会议

作者： Liangliang Lu Jiyang Dai Jin Ying Nanchang Hangkong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Multi-UAV cooperative trajectory planning faces issues such as model establishment difficulty and a considerable amount of *** work proposes a distributed hybrid particle swarm optimization(PSO) and differential evolution(DE) technique.A nonlinear time-varying PSO(NTVPSO) is developed to update the velocity and position of the PSO,aiming at the difficult balance of exploration and exploitation search for *** adaptive DE(ADE) is proposed in order to improve the information sharing between particles and the algorithm's convergence *** order to realize the collaborative planning of multiple UAVs,a distributed method is used to solve the single UAV trajectory planning task by using *** trace is fed back to the PSO velocity update formula to guide the direction of particle *** simulation results show that the algorithm is highly competitive in terms of path optimality and can be used as a viable alternative to solve multi-UAV cooperative trajectory planning problems.

关键词： Multu-UAV Particle Swarm Optimization Differential Evolution distributed method

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Decentralized UAV Swarm Scheduling with Constrained Task Exploration Balance

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DRONES 2023年第4期7卷 267-267页

作者： Chen, Runfeng Li, Jie Peng, Ting Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-Based Bundle Algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called "task consideration" is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster.

关键词： UAV planning swarm scheduling distributed method market-based algorithm task consideration

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A mutual-selecting market-based mechanism for dynamic coalition formation

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第1期15卷

作者： Xie, Bing Chen, Shaofei Chen, Jing Shen, LinCheng Natl Univ Def Technol Artificial Intelligence Lab Changsha Hunan Peoples R China

This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among distributed agents in this problem, and propose a negotiation mechanism for agents forming coalitions on timely emerging tasks. In this mechanism, we utilize an auction algorithm for real-time agent assignment on coalitions, and then design a mutual-selecting method to acquire better performance on agent utilization rate and task completion rate. And finally, our experimental results demonstrate that our market-based mechanism has a comparable performance in task completion rate to a decentralized approach (within 25% better on average) and a centralized dynamic coalition formation method (within 10% less on average performance).

关键词： Dynamic coalition formation distributed method market-based mechanism mutual-selecting multi-agent

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DRL-Based Improved UAV Swarm Control for Simultaneous Coverage and Tracking with Prior Experience Utilization

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DRONES 2024年第12期8卷 784-784页

作者： Chen, Yiting Chen, Runfeng Huang, Yuchong Xiong, Zehao Li, Jie Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Area coverage and target tracking are important applications of UAV swarms. However, attempting to perform both tasks simultaneously can be a challenge, particularly under resource constraints. In such scenarios, UAV swarms must collaborate to cover extensive areas while simultaneously tracking multiple targets. This paper proposes a deep reinforcement learning (DRL)-based, scalable UAV swarm control method for a simultaneous coverage and tracking (SCT) task, called the SCT-DRL algorithm. SCT-DRL simplifies the interaction between UAV swarms into a series of pairwise interactions and aggregates the information of perceived targets in advance, based on which forms the control framework with a variable number of neighboring UAVs and targets. Another highlight of SCT-DRL is using the trajectories of the traditional one-step optimization method to initialize the value network, which encourages the UAVs to select the actions leading to the state with less rest time to task completion to avoid extensive random exploration at the beginning of training. SCT-DRL can be seen as a special improvement of the traditional one-step optimization method, shaped by the samples derived from the latter, and gradually overcomes the inherent myopic issue with the far-sighted value estimation through RL training. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments.

关键词： deep reinforcement learning simultaneous coverage and tracking multi-UAV control distributed method

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distributed and communication-efficient solutions to linear equations with special sparse structure

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SYSTEMS & CONTROL LETTERS 2022年 160卷 105065-105065页

作者： Wang, Peng Gao, Yuanqi Yu, Nanpeng Ren, Wei Lian, Jianming Wu, Di Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manage Shanghai 200240 Peoples R China Univ Calif Riverside 900 Univ Ave Riverside CA 92521 USA Oak Ridge Natl Lab 1 Bethel Valley Rd Oak Ridge TN 37831 USA Pacific Northwest Natl Lab 902 Battelle Blvd Richland WA 99354 USA

In this paper, two distributed and communication-efficient algorithms based on the multi-agent system are proposed to solve a system of linear equations with the Laplacian sparse system matrix. One algorithm is based on the gradient descent method in optimization. In this algorithm, the agents only share partial information instead of all of their collective state vectors to save significant communication. The other algorithm is obtained by approximating Newton's method for a faster convergence rate. Although it requires twice as much communication as the first one, it is still communication-efficient given the low dimension of the information shared among agents. The convergence at a linear rate is proved for both algorithms, and a comprehensive comparison of their convergence rate, communication burden, and computation costs is also performed. The proposed algorithms can be applied to various systems to solve those problems that can be modeled as a system of linear equations with a Laplacian sparse system matrix. Simulation results with the electric power system illustrate their effectiveness. (C)& nbsp;2021 Published by Elsevier B.V.

关键词： distributed method Multi-agent system Linear equations Power system

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distributed minimum error entropy algorithms

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 4968-4998页

作者： Xin Guo Ting Hu Qiang Wu Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong China and School of Mathematics and Physics The University of Queensland Brisbane QLD Australia School of Mathematics and Statistics Wuhan University Wuhan China Department of Mathematical Sciences Middle Tennessee State University Murfreesboro TN

Minimum Error Entropy (MEE) principle is an important approach in Information Theoretical Learning (ITL). It is widely applied and studied in various fields for its robustness to noise. In this paper, we study a reproducing kernel-based distributed MEE algorithm, DMEE, which is designed to work with both fully supervised data and semi-supervised data. The divide-and-conquer approach is employed, so there is no inter-node communication overhead. Similar as other distributed algorithms, DMEE significantly reduces the computational complexity and memory requirement on single computing nodes. With fully supervised data, our proved learning rates equal the minimax optimal learning rates of the classical pointwise kernel-based regressions. Under the semi-supervised learning scenarios, we show that DMEE exploits unlabeled data effectively, in the sense that first, under the settings with weak regularity assumptions, additional unlabeled data significantly improves the learning rates of DMEE. Second, with su_cient unlabeled data, labeled data can be distributed to many more computing nodes, that each node takes only O(1) labels, without spoiling the learning rates in terms of the number of labels. This conclusion overcomes the saturation phenomenon in unlabeled data size. It parallels a recent results for regularized least squares (Lin and Zhou, 2018), and suggests that an ination of unlabeled data is a solution to the MEE learning problems with decentralized data source for the concerns of privacy protection. Our work refers to pairwise learning and non-convex loss. The theoretical analysis is achieved by distributed U-statistics and error decomposition techniques in integral operators.

关键词： information theoretic learning minimum error entropy distributed method semi-supervised data reproducing kernel Hilbert space

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Two-step market clearing for local energy trading in feeder-based markets

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JOURNAL OF ENGINEERING-JOE 2019年第18期2019卷 4775-4779页

作者： Khorasany, Mohsen Mishra, Yateendra Ledwich, Gerard Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia

Recent innovations in information and communication technologies (ICT) provide new opportunities and challenges for the integration of distributed energy resources (DERs) into the energy supply system as active market players. By increasing the integration of DERs, the novel market platform should be designed for these new market players to provide correct incentives for them to join the market and follow market rules. In this paper, a feeder-based market is proposed for local energy trading among prosumers and consumers in the distribution system to share energy with other players in the local market and with neighbourhood areas. A two-step market clearing (2SMC) mechanism is proposed for market clearing, in which in the first step, each local market is cleared independently to determine the market clearing price and in the second step, players can trade energy with neighbourhood areas. In comparison to a centralised market, the proposed method is scalable and reduces computation overheads, because instead of clearing market for a large number of players, the market is cleared for a fewer number of players. Case studies demonstrate the efficiency and effectiveness of the proposed market clearing method in increasing social welfare and reducing computation time.

关键词： power markets pricing distributed power generation feeder-based market distributed energy resources energy supply system market surplus market clearing price centralised market energy trading market players electricity market information and communication technologies DER distribution system two-step market clearing mechanism distributed method Lagrangian multipliers central computation centre private information

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distributed Low-Complexity Multi-cell Coordinated Multicast Beamforming with Large-Scale Antennas 19

Distributed Low-Complexity Multi-cell Coordinated Multicast ...

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19th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Yu, Jiawei Dong, Min Univ Ontario Inst Technol Dept Elect Comp & Software Engn Oshawa ON Canada

ISBN: (纸本)9781538635124

We consider downlink multicast beamforming in a multi-cell network with large-scale antennas. To reduce both communication and computation complexity in coordinated multicast beamforming, we propose the signal-to-leakage ratio (SLR) based coordinated multicast beamforming design to maximize the minimum SLR in the network. The SLR metric allows coordinated beamforming to be computed distributively and independently at each BS. To further reduce the complexity in obtaining the beam solution, we consider weighted maximum ratio transmission beamforming structure which transforms the multicast beamforming optimization into a weight optimization problem which is solved by the semi-definite relaxation approach. Our proposed distributed solution requires no communication or information sharing among cells and has very low computational complexity not depending on the number of antennas. Simulation shows that our proposed solution results in only a small loss to the centralized coordinated beamforming solution, but is significantly better than the non-coordinated methods.

关键词： Multicast beamforming massive MIMO multi-cell coordination distributed method maximum ratio transmission

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Penalty methods for distributed Optimization with Inequality and Equality Constraints

Penalty Methods for Distributed Optimization with Inequality...

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第40届中国控制会议

作者： Zicong Xia Yang Liu College of Mathematics and Computer Science Zhejiang Normal University

This paper focuses on the penalty method, which has the potential to turn constrained problems into unconstrained ones, for distributed optimization with inequality and equality constraints. A selection principle to choose penalty gains is proposed based on value-distance ratio. Furthermore, a distributed method is developed to solve the obtained problems while its convergence is rigorously proved. Finally, an example in a numerical way demonstrates the validity of this paper.

关键词： distributed optimization Penalty methods distributed method Non-smooth analysis

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Status and Analysis of Fault Diagnosis in Wireless Sensor Networks

Status and Analysis of Fault Diagnosis in Wireless Sensor Ne...

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作者： Yu-jie ZHU Ying-chun ZHONG Fang LI Jin-chao XIAO Guangdong University of Technology School of Automation Guangdong University of Finance & Economy Shenyang Institute of Automation Chinese Academy of Sciences

Wireless sensor networks(WSN) have great potential to support various applica-tions such as military, intelligent transportation, environmental monitoring, medical and health fields etc. It is the important for timely and accurate diagnosis of wireless sensor network faults to be the guarantee of reliability and stability. Based on the domestic and international research status of fault diagnosis technology, this paper summarized and analyzed the source and classification of wireless sensor network faults. And then the centralized techniques and distributed algorithms in the fault detection techniques were elaborated according to the main position of the diagnosis fault. Finally, the challenges of future research and development of sensor network fault diagnosis were discussed.

关键词： Wireless Sensor Network Fault Diagnosis Centralized method distributed method

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